{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "(tut_three)=\n",
    "\n",
    "# **Partial Network Quantization: Specific Layer Class**\n",
    "\n",
    "```{eval-rst}\n",
    "\n",
    ".. admonition:: Goal\n",
    "    :class: note\n",
    "\n",
    "    #. Take a resnet-like model and train on cifar10 dataset.\n",
    "    #. Quantize only 'Dense' layer class.\n",
    "    #. Fine-tune to recover model accuracy.\n",
    "    #. Save both original and quantized model while performing ONNX conversion.\n",
    "\n",
    "```\n",
    "\n",
    "```{eval-rst}\n",
    "\n",
    ".. admonition:: Background\n",
    "    :class: tip\n",
    "\n",
    "    Specific layer classes to quantize are passed to `quantize_model()` via a `QuantizationSpec` object.\n",
    "    For layer `l`, the class name can be found using `l.__class__.__name__`. </br>\n",
    "\n",
    "```\n",
    "Refer to the [Python API](qmodel_api) documentation for more details.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#\n",
    "# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n",
    "# SPDX-License-Identifier: Apache-2.0\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "#\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow_quantization import quantize_model, QuantizationSpec\n",
    "import tiny_resnet\n",
    "from tensorflow_quantization import utils\n",
    "import os\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "# Create folders to save TF and ONNX models\n",
    "assets = utils.CreateAssetsFolders(os.path.join(os.getcwd(), \"tutorials\"))\n",
    "assets.add_folder(\"simple_network_quantize_specific_class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Load CIFAR10 dataset\n",
    "cifar10 = tf.keras.datasets.cifar10\n",
    "(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()\n",
    "\n",
    "# Normalize the input image so that each pixel value is between 0 and 1.\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_model_original = tiny_resnet.model()\n",
    "tf.keras.utils.plot_model(nn_model_original, to_file = assets.simple_network_quantize_specific_class.fp32 + \"/model.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1407/1407 [==============================] - 17s 10ms/step - loss: 1.7871 - accuracy: 0.3526 - val_loss: 1.5601 - val_accuracy: 0.4448\n",
      "Epoch 2/10\n",
      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.4970 - accuracy: 0.4641 - val_loss: 1.4441 - val_accuracy: 0.4812\n",
      "Epoch 3/10\n",
      "1407/1407 [==============================] - 13s 9ms/step - loss: 1.3885 - accuracy: 0.5040 - val_loss: 1.3627 - val_accuracy: 0.5178\n",
      "Epoch 4/10\n",
      "1407/1407 [==============================] - 13s 10ms/step - loss: 1.3101 - accuracy: 0.5347 - val_loss: 1.3018 - val_accuracy: 0.5332\n",
      "Epoch 5/10\n",
      "1407/1407 [==============================] - 13s 9ms/step - loss: 1.2473 - accuracy: 0.5591 - val_loss: 1.2233 - val_accuracy: 0.5650\n",
      "Epoch 6/10\n",
      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.1926 - accuracy: 0.5796 - val_loss: 1.2065 - val_accuracy: 0.5818\n",
      "Epoch 7/10\n",
      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.1475 - accuracy: 0.5972 - val_loss: 1.1449 - val_accuracy: 0.5966\n",
      "Epoch 8/10\n",
      "1407/1407 [==============================] - 13s 10ms/step - loss: 1.1041 - accuracy: 0.6126 - val_loss: 1.1292 - val_accuracy: 0.6048\n",
      "Epoch 9/10\n",
      "1407/1407 [==============================] - 14s 10ms/step - loss: 1.0636 - accuracy: 0.6275 - val_loss: 1.1122 - val_accuracy: 0.6112\n",
      "Epoch 10/10\n",
      "1407/1407 [==============================] - 13s 10ms/step - loss: 1.0268 - accuracy: 0.6406 - val_loss: 1.0829 - val_accuracy: 0.6244\n"
     ]
    }
   ],
   "source": [
    "# Train original classification model\n",
    "nn_model_original.compile(\n",
    "    optimizer=tiny_resnet.optimizer(lr=1e-4),\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=[\"accuracy\"],\n",
    ")\n",
    "\n",
    "_ = nn_model_original.fit(\n",
    "    train_images, train_labels, batch_size=32, epochs=10, validation_split=0.1\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline FP32 model test accuracy: 61.51\n"
     ]
    }
   ],
   "source": [
    "# Get baseline model accuracy\n",
    "_, baseline_model_accuracy = nn_model_original.evaluate(\n",
    "    test_images, test_labels, verbose=0\n",
    ")\n",
    "baseline_model_accuracy = round(100 * baseline_model_accuracy, 2)\n",
    "print(\"Baseline FP32 model test accuracy:\", baseline_model_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /home/sagar/nvidia/2021/Customers/Waymo/QAT/tensorrt_qat/docs/source/notebooks/tutorial_onnx_models/simple_network_quantize_specific_class/fp32/saved_model/assets\n",
      "WARNING:tensorflow:From /home/sagar/miniconda3/lib/python3.8/site-packages/tf2onnx/tf_loader.py:711: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
      "ONNX conversion Done!\n"
     ]
    }
   ],
   "source": [
    "# Save TF FP32 original model\n",
    "tf.keras.models.save_model(nn_model_original, assets.simple_network_quantize_specific_class.fp32_saved_model)\n",
    "\n",
    "# Convert FP32 model to ONNX\n",
    "utils.convert_saved_model_to_onnx(saved_model_dir = assets.simple_network_quantize_specific_class.fp32_saved_model, onnx_model_path = assets.simple_network_quantize_specific_class.fp32_onnx_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I] Layer class `Conv2D` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `ReLU` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `Conv2D` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `ReLU` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `Conv2D` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `ReLU` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `Conv2D` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `Add` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `ReLU` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `Conv2D` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `ReLU` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `Conv2D` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `Conv2D` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `Add` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `ReLU` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `MaxPooling2D` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `Flatten` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n",
      "[I] Layer class `ReLU` is not quantized. Partial quantization is enabled and layer class is not in user provided QuantizationSpec class object\n"
     ]
    }
   ],
   "source": [
    "# Quantize model\n",
    "# 1.1 Create a list with keras layer classes to quantize\n",
    "qspec = QuantizationSpec()\n",
    "qspec.add(name=\"Dense\", is_keras_class=True)\n",
    "# 1.2 Call quantize model function\n",
    "q_nn_model = quantize_model(model=nn_model_original, quantization_mode='partial', quantization_spec=qspec)\n",
    "\n",
    "q_nn_model.compile(\n",
    "    optimizer=tiny_resnet.optimizer(lr=1e-4),\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=[\"accuracy\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy immediately after quantization:60.28, diff:1.2299999999999969\n"
     ]
    }
   ],
   "source": [
    "_, q_model_accuracy = q_nn_model.evaluate(test_images, test_labels, verbose=0)\n",
    "q_model_accuracy = round(100 * q_model_accuracy, 2)\n",
    "print(\n",
    "    \"Test accuracy immediately after quantization:{}, diff:{}\".format(\n",
    "        q_model_accuracy, (baseline_model_accuracy - q_model_accuracy)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(q_nn_model, to_file = assets.simple_network_quantize_specific_class.int8 + \"/model.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9981 - accuracy: 0.6521 - val_loss: 1.0761 - val_accuracy: 0.6324\n",
      "Epoch 2/2\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9655 - accuracy: 0.6631 - val_loss: 1.0572 - val_accuracy: 0.6302\n",
      "Accuracy after fine tuning for 2 epochs :61.82\n"
     ]
    }
   ],
   "source": [
    "# Fine-tune quantized model\n",
    "fine_tune_epochs = 2\n",
    "q_nn_model.fit(\n",
    "    train_images,\n",
    "    train_labels,\n",
    "    batch_size=32,\n",
    "    epochs=fine_tune_epochs,\n",
    "    validation_split=0.1,\n",
    ")\n",
    "_, q_model_accuracy = q_nn_model.evaluate(test_images, test_labels, verbose=0)\n",
    "q_model_accuracy = round(100 * q_model_accuracy, 2)\n",
    "print(\n",
    "    \"Accuracy after fine tuning for {} epochs :{}\".format(\n",
    "        fine_tune_epochs, q_model_accuracy\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as dense_layer_call_fn, dense_layer_call_and_return_conditional_losses, dense_1_layer_call_fn, dense_1_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /home/sagar/nvidia/2021/Customers/Waymo/QAT/tensorrt_qat/docs/source/notebooks/tutorial_onnx_models/simple_network_quantize_specific_class/int8/saved_model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /home/sagar/nvidia/2021/Customers/Waymo/QAT/tensorrt_qat/docs/source/notebooks/tutorial_onnx_models/simple_network_quantize_specific_class/int8/saved_model/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ONNX conversion Done!\n"
     ]
    }
   ],
   "source": [
    "# Save TF INT8 original model\n",
    "tf.keras.models.save_model(q_nn_model, assets.simple_network_quantize_specific_class.int8_saved_model)\n",
    "\n",
    "# Convert INT8 model to ONNX\n",
    "utils.convert_saved_model_to_onnx(saved_model_dir = assets.simple_network_quantize_specific_class.int8_saved_model, onnx_model_path = assets.simple_network_quantize_specific_class.int8_onnx_model)\n",
    "\n",
    "tf.keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "```{note}\n",
    "ONNX files can be visualized with [Netron](https://netron.app/).\n",
    "```"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.8.10 ('base')",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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